Minimizing Schedule Length Research Articles

A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms

A cloud computing environment processes user workloads or tasks by exploiting its high performance computational, storage, of reducing and network resources. The virtual machines in the cloud environment are allocated to tasks with the aim of reducing overall execution time. The use of high performance resources incurs monetary costs, as well as high power consumption. The heuristic based approaches implemented for scheduling tasks are unable to cope with the complexity of optimizing multiple parameters. In this paper, we propose a multi-objective grey-wolf optimization based algorithm for scheduling tasks on cloud platforms. The proposed algorithm targets to minimize schedule length (overall execution time), energy consumption, and monetary cost required for executing tasks. For optimization, the algorithm incorporates steps that are performed iteratively for mimicking the behavior of grey wolves attacking their prey. It uses discrete values for positioning wolves for encircling and attacking the prey. The assignment of tasks to virtual machines is performed using the solution found after multi-objective optimization that incorporates weighted sorting for arranging solutions. Our experimentation performed using the CloudSim framework shows that the proposed algorithm outperforms other algorithms with performance improvement ranging from 3.98% to 16.07%, while considering the schedule length, monetary cost, and energy consumption.

Minimum Length Scheduling for Discrete-Rate Full-Duplex Wireless Powered Communication Networks

Wireless powered communication networks (WPCNs) will act as a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. The MTC networks will be deployed by using low-power transceivers with finite discrete configurations. This paper considers minimum length scheduling problem for full-duplex WPCNs, where users transmit information to a hybrid access point at a rate chosen from a finite set of discrete-rate levels. The optimization problem considers energy causality, data and maximum transmit power constraints, and is proven to be NP-hard. As a solution strategy, we define the minimum length scheduling (MLS) slot, which is slot of minimum transmission completion time while starting transmission at anytime after the decision time. We solve the problem optimally for a given transmission order based on the optimality analysis of MLS slot. For the general problem, we categorize the problem based on whether the MLS slots of users overlap over time. We propose optimal algorithm for non-overlapping scenario by allocating the MLS slots, and a polynomial-time heuristic algorithm for overlapping scenario by allocating the transmission slot to the user with earliest MLS slot. Through simulations, we demonstrate significant gains of scheduling and discrete rate allocation.

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of high-performance distributed computing and it provides resource polling and on-demand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

Minimum Length Scheduling for Multi-Cell Full Duplex Wireless Powered Communication Networks.

Wireless powered communication networks (WPCNs) will be a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. These MTC networks will be deployed by using low-power transceivers and a very limited set of transmission configurations. We investigate a novel minimum length scheduling problem for multi-cell full-duplex wireless powered communication networks to determine the optimal power control and scheduling for constant rate transmission model. The formulated optimization problem is combinatorial in nature and, thus, difficult to solve for the global optimum. As a solution strategy, first, we decompose the problem into the power control problem (PCP) and scheduling problem. For the PCP, we propose the optimal polynomial time algorithm based on the evaluation of Perron–Frobenius conditions. For the scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. Through extensive simulations, we demonstrate a 50% reduction in the schedule length by using the proposed algorithm in comparison to unscheduled concurrent transmissions.

Scheduling Multiprocessor Tasks with Equal Processing Times as a Mixed Graph Coloring Problem

This article extends the scheduling problem with dedicated processors, unit-time tasks, and minimizing maximal lateness Lmax for integer due dates to the scheduling problem, where along with precedence constraints given on the set V={v1,v2, …,vn} of the multiprocessor tasks, a subset of tasks must be processed simultaneously. Contrary to a classical shop-scheduling problem, several processors must fulfill a multiprocessor task. Furthermore, two types of the precedence constraints may be given on the task set V. We prove that the extended scheduling problem with integer release times ri≥0 of the jobs V to minimize schedule length Cmax may be solved as an optimal mixed graph coloring problem that consists of the assignment of a minimal number of colors (positive integers) {1,2, …,t} to the vertices {v1,v2, …,vn}=V of the mixed graph G=(V,A, E) such that, if two vertices vp and vq are joined by the edge [vp,vq]∈E, their colors have to be different. Further, if two vertices vi and vj are joined by the arc (vi,vj)∈A, the color of vertex vi has to be no greater than the color of vertex vj. We prove two theorems, which imply that most analytical results proved so far for optimal colorings of the mixed graphs G=(V,A, E), have analogous results, which are valid for the extended scheduling problems to minimize the schedule length or maximal lateness, and vice versa.

An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.

Minimum Length Scheduling for Full Duplex Time-Critical Wireless Powered Communication Networks

Radio frequency (RF) energy harvesting is key in attaining perpetual lifetime for time-critical wireless powered communication networks (WPCNs) due to full control on energy transfer, far field region, small and low-cost circuitry. In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex WPCN. We first formulate the problem as a mixed integer non-linear programming problem and conjecture that the problem is NP-hard. As a solution strategy, we demonstrate that the power control and time allocation, and the scheduling problems can be solved separately in the optimal solution. For the power control and time allocation problem, we derive the optimal solution by evaluating Karush-Kuhn-Tucker conditions. For the scheduling, we introduce a penalty function allowing reformulation of the problem as a sum penalty minimization problem. Upon derivation of the optimality conditions based on the characteristics of the penalty function, we propose two polynomial-time heuristic algorithms and a reduced-complexity exact algorithm employing smart pruning techniques. Via extensive simulations, we illustrate that the proposed heuristic schemes outperform the schemes for predetermined transmission order of users and achieve close-to-optimal solutions.

A heuristic approach for scheduling activities with ‘OR’-precedence constraints at an underground mine

ABSTRACT Medium-term development planning of underground mines requires scheduling multiple activities to comply with long-term milestones, and to obtain a time span as short as possible. However, the planning must also respect the availability of construction resources and precedence constraints, which in our case can be disjunctive, that is with more than one alternative predecessor. In this paper, we present an optimisation model to find the schedule of minimum length, satisfying all the constraints mentioned. We develop a heuristic approach to solve it and show that it can be used to produce feasible development plans in a real mine.

Fair budget constrained workflow scheduling approach for heterogeneous clouds

The phenomenal advancement of technology paved the way for the execution of complex scientific applications. The emergence of the cloud provides a distributed heterogeneous environment for the execution of large and complex workflows. Due to the dynamic and heterogeneous nature of the cloud, scheduling workflows become a challenging problem. Mapping and assignment of heterogeneous instances for each task while minimizing execution time and cost is a NP-complete problem. For efficient scheduling, it is required to consider various QoS parameters such as time, cost, security, and reliability. Among these, computation time and cost are the two notable parameters. In order to preserve the functionalities of these two parameters in heterogeneous cloud environments, in this paper, a fair budget-constrained workflow scheduling algorithm (FBCWS) is proposed. The novelty of the proposed algorithm is to minimize the makespan while satisfying budget constraints and a fair means of schedule for every task. FBCWS also provides a mechanism to save budget by adjusting the cost-time efficient factor of the minimization problem. The inclusion of a cost-time efficient factor in the algorithm provides flexibility to minimize the makespan or save budget. In order to validate the effectiveness of the proposed approach, several real scientific workflows are simulated, and experimental results are compared with other existing approaches, namely; Heterogeneous Budget Constrained Scheduling (HBCS), Minimizing Schedule Length using Budget Level (MSBL) and Pareto Optimal Scheduling Heuristic (POSH) algorithms. Experimental results prove that the proposed algorithm behaves outstandingly for compute-intensive workflows such as Epigenomic and Sipht. Also, FBCWS outperforms the existing HBCS in most of the cases. Moreover, FBCWS proves to be more time-efficient than POSH and more cost-efficient than MSBL. The effectiveness of the proposed algorithm is illustrated through the popular ANOVA test.

GLS and VNS based heuristics for conflict-free minimum-latency aggregation scheduling in WSN

ABSTRACT We consider a conflict-free minimum latency data aggregation problem that occurs in different wireless networks. Given a network that is presented as an undirected graph with one selected vertex (a sink), the goal is to find a spanning aggregation tree rooted in the sink and to define a conflict-free aggregation minimum length schedule along the arcs of the tree directed to the sink. Herewith, at the same time slot, each element of the network can either send or receive at most one message. Only one message should be sent by each network element during the whole aggregation session, and the conflicts caused by signal interference should be excluded. This problem is NP-hard and remains NP-hard even in the case when the aggregation tree is given. Therefore, the development of efficient approximation algorithms is very important for this problem. In this paper, we present new heuristic algorithms based on genetic local search and variable neighbourhood search metaheuristics. We conducted an extensive simulation that demonstrates the superiority of our algorithms compared with the best of the previous approaches.

PARALLEL PROCESSORS SCHEDULING ALGORITHMS TO MINIMISE MAKESPAN IN A GALVANISING PLANT

Ponte Academic JournalMar 2020, Volume 76, Issue 3 PARALLEL PROCESSORS SCHEDULING ALGORITHMS TO MINIMISE MAKESPAN IN A GALVANISING PLANTAuthor(s): Mendon Dewa ,Bakhe NleyaJ. Ponte - Mar 2020 - Volume 76 - Issue 3 doi: 10.21506/j.ponte.2020.3.18 Abstract:Galvanising lines consists of load/loading stations and a series of processing tanks that are generally energy-intensive. Each raw part needs to go through a number of processing stages sequentially. Job sizes and processing time vary from part to part, hence the need to derive an optimal schedule to minimise total processing time in a batch. The problem of minimizing the makespan on parallel processing machines using different scheduling algorithms is studied in this paper. A set of independent tasks were scheduled on parallel processors in order to minimize schedule length using Integer Linear Programming, Shortest Processing Time, Longest Processing Time, and Greedy Genetic algorithms. The experimental results demonstrated that our Greedy Genetic algorithm outperformed other algorithms on minimizing makespan on parallel processing machines. Download full text:Check if you have access through your login credentials or your institution Username Password

Minimizing end-to-end delay in multi-hop wireless networks with optimized transmission scheduling

The problem of transmission scheduling in single hop and multi-hop wireless networks has been extensively studied. The focus has been on optimizing the efficiency of transmission parallelization, through a minimum-length schedule that meets a given set of traffic demands using the smallest possible number of time slots. Each time slot is associated with a set of transmissions that are compatible with each other according to the considered interference model. The minimum-length approach maximizes the resource reuse, but it does not ensure minimum end-to-end packet delay for multiple source-destination pairs, due to its inherent assumption of frame periodicity. In the paper we study the problem of transmission scheduling and routing aiming at minimizing the end-to-end delay under the signal-to-interference-and-noise-ratio (SINR) model for multi-hop networks. Two schemes are investigated. The first scheme departs from the conventional scheduling approach, by addressing explicitly end-to-end delay and removing the restriction of frame periodicity. The second scheme extends the first one by featuring cooperative forwarding and forward interference cancellation. We study the properties of the two schemes, and propose novel mixed-integer programming models and solution algorithms. Extensive results are provided to gain insights on how the schemes perform in end-to-end delay.

A novel scheduling with multi-criteria for high-performance computing systems: an improved genetic algorithm-based approach

Scheduling in high-performance computing systems is experiencing potential challenges in modern computing applications due to different application sizes, computational requirements, resource utilization, rational completion time, etc. The scheduling problem is known to be an NP-complete problem. These challenges are moderated by the logical assignment of tasks to processors in a way to produce minimum schedule length and lesser load balance by utilizing system resources. In this paper, we proposed a novel genetic algorithm (GA)-based scheduling technique by considering four conflicting objectives, minimization of makespan, load balancing, and maximization of resource utilization, and speed up ratio. A novel mutation technique is proposed which helps to improve the considered multiple objectives. The performance of the proposed work is analyzed and validated through extensive simulation results using synthetic as well as benchmark data sets. It has been observed that the proposed work performs better than the existing algorithms, GA-based scheduling, priority-based performance-improved algorithm, and particle swarm optimization. A statistical hypothesis test ANOVA followed by post hoc analysis is conducted to demonstrate the significance of the work.

Optimally Removing Synchronization Overhead for CNNs in Three-Dimensional Neuromorphic Architecture

Recent three-dimensional (3-D) neuromorphic processing-in-memory (PIM) architecture provides a promising hardware-based solution to speed up the processing of convolutional neural networks. However, the limited capacity of the global buffer in this architecture is unable to efficiently handle synchronization overhead. In this paper, we jointly optimize the allocation of computation and memory resources on the 3-D-stacked PIM architecture. The objective is to minimize schedule length by removing synchronization overhead. To guarantee the generation of a feasible task schedule, we theoretically obtain the upper bound to reschedule each computation task. The target problem is further formulated as a dynamic programming model to get an optimal solution. We evaluate our technique with a variety of realistic neural network applications running on deep learning frameworks Caffe and TensorFlow. The results show that the proposed technique can achieve a significant reduction in processing time and improve the utilization of processing cores compared to previous studies.

Optimal task execution speed setting and lower bound for delay and energy minimization

The current technology trend reveals that static power consumption is growing at a faster rate than dynamic power consumption. In this paper, energy-efficient task scheduling is studied when static power consumption is a significant part of energy consumption which cannot be ignored. The problems of scheduling a set of independent sequential tasks on identical processors so that the schedule length is minimized for a given energy consumption constraint or the energy consumption is minimized for a given schedule length constraint are investigated. For a given schedule, the optimal task execution speed setting for delay and energy minimization is found analytically. Lower bounds for the minimum schedule length of a set of tasks with a given energy consumption constraint and the minimum energy consumption of a set of tasks with a given schedule length constraint are established. Our lower bounds are applicable to sequential or parallel, and independent or precedence constrained tasks, on processors with discrete or continuous speed levels, and bounded or unbounded speed ranges. The significance of these lower bounds is that they can be used to evaluate the performance of any heuristic algorithms when compared with optimal algorithms. Experimental study on the performance of list scheduling algorithms is performed and it is shown that their performance is very close to the optimal. To the best of the author’s knowledge, this is the first paper that provides such analytical results for energy-efficient task scheduling with both dynamic and static power consumptions.

BFCO: A BPSO-Based Fine-Grained Communication Optimization Method for MPSoC

Fine-grained multithreaded models can provide more opportunities for system performance improvements, but they face large amount of communication overhead. To reduce the communication overhead to improve performance, message aggregation and communication pipeline have been proposed and their advantages have been discussed in previous works. However, when exploiting them in real-life applications, it is challenging to maximize their advantages due to their unrevealed problems. This paper investigates the two communication optimization techniques from the view of their problems to fulfill the studies. We have found that when applying the techniques, message aggregation may have the deadlock problem and latency problem, and communication pipeline may face the ineffective problem and prolog and epilog problem. Based on the basic requirement of allocating the two techniques on an initial schedule, we then present general solutions to avoid or handle the problems. To obtain a minimum schedule length for an initial schedule, we further develop a binary particle swarm optimization-based fine-grained communication optimization method to allocate message aggregation and communication pipeline for best performance. The method is also integrated into light and efficient simulink compiler for embedded application multithreaded code generator to generate high quality codes. Experimental results on both synthetic and real-life applications demonstrate the efficiency of the proposed method.

Foreword to the special issue on networked system security and efficiency

Foreword to the special issue on networked system security and efficiency

Scheduling parallel and distributed processing for automotive data stream management system

In this paper, to analyze end-to-end timing behavior in heterogeneous processor and network environments accurately, we adopt and modify a heterogeneous selection value on communication contention (HSV_CC) algorithm, which can synchronize tasks and messages simultaneously, for stream processing distribution. In order to adapt the concepts of a static algorithm like HSV_CC to automotive data stream management system (DSMSs), one must first address three issues: (i) previous task and message schedules might lead to less efficient resource usages in this scenario; (ii) the conventional method to determine the task scheduling order may not be best suited to deal with stream processing graphs, and; (iii) there is a need to be able to schedule tasks with time-varying computational requirements efficiently. To address (i), we propose the heterogeneous value with load balancing and communication contention (HVLB_CC) (A) algorithm, which considers load balancing in addition to the parameters considered by the HSV_CC algorithm. We propose HVLB_CC (B) to address issue (ii). HVLB_CC (B) can deal with stream processing task graphs and more various directed acyclic graphs to prevent assigning a higher priority to successor tasks. In addition, to address issue (iii), we propose HVLB_CC_IC. To schedule tasks more efficiently with various computation times, HVLB_CC_IC utilizes schedule holes left in processors. These idle time slots can be used for the execution of an optional part to generate more precise data results by applying imprecise computation models. Experimental results demonstrate that the proposed algorithms improve minimum schedule length, accuracy, and load balancing significantly compared to the HSV_CC algorithm. In addition, the proposed HVLB_CC (B) algorithm can schedule more varied task graphs without reducing performance, and, using imprecise computation models, HVLB_CC_IC yields higher precision data than HVLB_CC without imprecise computation models.

Prior node selection for scheduling workflows in a heterogeneous system

Many workflow scheduling algorithms for heterogeneous systems have been developed to satisfy multiple requirements such as minimizing schedule length while maximizing throughput. In particular, in list-based scheduling approaches, the schedule length depends on the given nodes as well as the task allocation and ordering policies. This is because the scheduling priority is derived by averaging the execution time and communication time of the given nodes. If the set of nodes can be adjusted before the scheduling tasks, a small schedule length can be achieved. In this paper, we propose a prior node selection algorithm, called lower bound based candidate node selection (LBCNS) to select a subset of given nodes to minimize the schedule length while fairly scheduling each job. Our proposal has two approaches: (i) LBCNS_DEFAULT, which considers the job characteristics and each node’s performance, and (ii) priority-based LBCNS, which additionally takes each scheduling priority into account for a dedicated task scheduling algorithm.The experimental results of extensive simulations show that LBCNS_DEFAULT has the best fairness for scheduling multiple workflow jobs, while priority-based LBCNS achieves the minimum schedule length with the highest efficiency for a single workflow job and multiple workflow jobs.

Scheduling of Identical Jobs with Bipartite Incompatibility Graphs on Uniform Machines. Computational Experiments

Abstract. In the paper we consider the problem of scheduling of unit-length jobs on 3 or 4 uniform parallel machines to minimize schedule length or total completion time. We assume that jobs are subject to some kind of mutual exclusion constraints, modeled by a bipartite graph of bounded degree. The edges of the graph correspond to pairs of jobs that cannot be processed on the same machine. Although the problem is generally NP-hard, we show that under some conditions imposed on machine speeds and the structure of incompatibility graph our problem can be solved to optimality in polynomial time. Theoretical considerations are accompanied by computer experiments with some particular model of scheduling.